# Sample Vector IO Configuration with Configurable Embedding Models
#
# This example demonstrates how to configure embedding models for different vector IO providers.
# Each provider can have its own default embedding model and dimension configuration.

# Vector IO providers with different embedding configurations
vector_io:
  # Fast local search with lightweight embeddings
  - provider_id: fast_local_search
    provider_type: inline::faiss
    config:
      db_path: ~/.llama/distributions/together/faiss_fast.db
      # Use lightweight embedding model for fast processing
      embedding_model: "all-MiniLM-L6-v2"
      embedding_dimension: 384  # Fixed dimension for this model

  # Compact storage with variable dimension embeddings
  - provider_id: compact_storage
    provider_type: inline::faiss
    config:
      db_path: ~/.llama/distributions/together/faiss_compact.db
      # Use Matryoshka embeddings with custom dimension
      embedding_model: "nomic-embed-text"
      embedding_dimension: 256  # Reduced from default 768 for storage efficiency

  # High-quality persistent search
  - provider_id: persistent_search
    provider_type: inline::sqlite_vec
    config:
      db_path: ~/.llama/distributions/together/sqlite_vec.db
      # Use high-quality embedding model
      embedding_model: "sentence-transformers/all-mpnet-base-v2"
      embedding_dimension: 768  # Full dimension for best quality

  # Remote Qdrant with cloud embeddings
  - provider_id: cloud_search
    provider_type: remote::qdrant
    config:
      api_key: "${env.QDRANT_API_KEY}"
      url: "${env.QDRANT_URL}"
      # Use OpenAI embeddings for cloud deployment
      embedding_model: "text-embedding-3-small"
      embedding_dimension: 1536  # OpenAI's default dimension

  # Remote ChromaDB without explicit embedding config (uses system default)
  - provider_id: default_search
    provider_type: remote::chroma
    config:
      host: "${env.CHROMA_HOST:=localhost}"
      port: 8000
      # No embedding_model specified - will use system default from model registry

  # Milvus with production-grade configuration
  - provider_id: production_search
    provider_type: remote::milvus
    config:
      uri: "${env.MILVUS_ENDPOINT}"
      token: "${env.MILVUS_TOKEN}"
      kvstore:
        type: sqlite
        db_path: ~/.llama/distributions/together/milvus_registry.db
      # High-performance embedding model for production
      embedding_model: "text-embedding-3-large"
      embedding_dimension: 3072  # Large dimension for maximum quality

# Model registry - ensure embedding models are properly configured
models:
  # Lightweight embedding model (384 dimensions)
  - model_id: all-MiniLM-L6-v2
    provider_id: local_inference
    provider_model_id: sentence-transformers/all-MiniLM-L6-v2
    model_type: embedding
    metadata:
      embedding_dimension: 384
      description: "Fast, lightweight embeddings for general use"

  # Matryoshka embedding model (variable dimensions)
  - model_id: nomic-embed-text
    provider_id: local_inference
    provider_model_id: nomic-embed-text
    model_type: embedding
    metadata:
      embedding_dimension: 768  # Default, can be overridden
      description: "Flexible Matryoshka embeddings supporting variable dimensions"

  # High-quality embedding model (768 dimensions)
  - model_id: sentence-transformers/all-mpnet-base-v2
    provider_id: local_inference
    provider_model_id: sentence-transformers/all-mpnet-base-v2
    model_type: embedding
    metadata:
      embedding_dimension: 768
      description: "High-quality embeddings for semantic search"

  # OpenAI embedding models (for cloud usage)
  - model_id: text-embedding-3-small
    provider_id: openai_inference  # Would need OpenAI provider configured
    provider_model_id: text-embedding-3-small
    model_type: embedding
    metadata:
      embedding_dimension: 1536  # Default OpenAI dimension
      description: "OpenAI's efficient embedding model"

  - model_id: text-embedding-3-large
    provider_id: openai_inference
    provider_model_id: text-embedding-3-large
    model_type: embedding
    metadata:
      embedding_dimension: 3072  # Large dimension for maximum quality
      description: "OpenAI's highest quality embedding model"

# Optional: Configure specific vector databases (will use provider defaults)
vector_dbs:
  # Uses fast_local_search provider defaults (all-MiniLM-L6-v2, 384 dims)
  - vector_db_id: general_docs
    provider_id: fast_local_search

  # Uses compact_storage provider defaults (nomic-embed-text, 256 dims)
  - vector_db_id: compressed_knowledge
    provider_id: compact_storage

  # Uses persistent_search provider defaults (all-mpnet-base-v2, 768 dims)
  - vector_db_id: semantic_library
    provider_id: persistent_search

# Server configuration
server:
  host: 0.0.0.0
  port: 5000

# Logging configuration
logging:
  level: INFO